恢复检查点，以重新训练新班级

Question

我有一个检查点，它是用11个班级训练的。我添加了一个类到我的数据集，并试图恢复它以保留CNN，但它给了我一个与形状有关的错误，因为前一个类是用11个类训练的，实际上有12个类，我是否保存了权重和偏差变量以正确的方式？我该怎么办 ？这里是代码：

batch_size = 10 
num_hidden = 64 
num_channels = 1 
depth = 32 
.... 
graph = tf.Graph() 

with graph.as_default(): 

# Input data. 
    tf_train_dataset = tf.placeholder(
    tf.float32, shape=(batch_size, IMAGE_SIZE_H, IMAGE_SIZE_W, num_channels)) 
    tf_train_labels = tf.placeholder(tf.float32, shape=(batch_size, num_labels)) 
    tf_valid_dataset = tf.constant(valid_dataset) 
    tf_test_dataset = tf.constant(test_dataset) 

    w_b = { 
    'weight_0': tf.Variable(tf.random_normal([patch_size_1, patch_size_1, num_channels, depth],stddev=0.1)), 
    'weight_1': tf.Variable(tf.random_normal([patch_size_2, patch_size_2, depth, depth], stddev=0.1)), 
    'weight_2': tf.Variable(tf.random_normal([patch_size_3, patch_size_3, depth, depth], stddev=0.1)), 
    'weight_3': tf.Variable(tf.random_normal([IMAGE_SIZE_H // 32 * IMAGE_SIZE_W // 32 * depth, num_hidden], stddev=0.1)), 
    'weight_4': tf.Variable(tf.random_normal([num_hidden, num_labels], stddev=0.1)), 

    'bias_0' : tf.Variable(tf.zeros([depth])), 
    'bias_1' : tf.Variable(tf.constant(1.0, shape=[depth])), 
    'bias_2' : tf.Variable(tf.constant(1.0, shape=[depth])), 
    'bias_3' : tf.Variable(tf.constant(1.0, shape=[num_hidden])), 
    'bias_4' : tf.Variable(tf.constant(1.0, shape=[num_labels])) 
     } 

# Model. 
    def model(data): 

     conv_1 = tf.nn.conv2d(data, w_b['weight_0'] , [1, 2, 2, 1], padding='SAME') 
     hidden_1 = tf.nn.relu(conv_1 + w_b['bias_0']) 
     pool_1 = tf.nn.max_pool(hidden_1,ksize = [1,5,5,1], strides= [1,2,2,1],padding ='SAME') 
     conv_2 = tf.nn.conv2d(pool_1, w_b['weight_1'], [1, 2, 2, 1], padding='SAME') 
     hidden_2 = tf.nn.relu(conv_2 + w_b['bias_1']) 
     conv_3 = tf.nn.conv2d(hidden_2, w_b['weight_2'], [1, 2, 2, 1], padding='SAME') 
     hidden_3 = tf.nn.relu(conv_3 + w_b['bias_2']) 
     pool_2 = tf.nn.max_pool(hidden_3,ksize = [1,3,3,1], strides= [1,2,2,1],padding ='SAME') 
     shape = pool_2.get_shape().as_list() 
     reshape = tf.reshape(pool_2, [shape[0], shape[1] * shape[2] * shape[3]]) 
     hidden_4 = tf.nn.relu(tf.matmul(reshape, w_b['weight_3']) + w_b['bias_3']) 

     return tf.matmul(hidden_4, w_b['weight_4']) + w_b['bias_4'] 


    # Training computation. 
    logits = model(tf_train_dataset) 

    loss = tf.reduce_mean(
    tf.nn.softmax_cross_entropy_with_logits(logits, tf_train_labels)) 


    optimizer = tf.train.GradientDescentOptimizer(0.01).minimize(loss) 



    train_prediction = tf.nn.softmax(logits) 
    valid_prediction = tf.nn.softmax(model(tf_valid_dataset)) 
    test_prediction = tf.nn.softmax(model(tf_test_dataset)) 

    init = tf.initialize_all_variables() 
    w_b_saver = tf.train.Saver(var_list = w_b) 

num_steps = 1001 


with tf.Session(graph=graph) as sess: 
ckpt = ("/home/..../w_b_models.ckpt") 
if os.path.isfile(ckpt) : 

    w_b_saver.restore(sess,ckpt) 
    print("restore complete") 
    print('Test accuracy: %.1f%%' % accuracy(test_prediction.eval() , test_labels)) 
else: 
    print("Error while loading model checkpoint.") 

    print('Initialized') 
    sess.run(init) 

    for step in range(num_steps): 
     ..... 

    accuracy(test_prediction.eval(),test_labels, force = False)) 
    save_path_w_b = w_b_saver.save(sess, "/home/...../w_b_models.ckpt") 
    print("Model saved in file: %s" % save_path_w_b) 

，这里是错误：

InvalidArgumentError: Assign requires shapes of both tensors to match. lhs shape= [64,12] rhs shape= [64,11] 
[[Node: save/Assign_9 = Assign[T=DT_FLOAT, _class=["loc:@Variable_4"], use_locking=true, validate_shape=true, _device="/job:localhost/replica:0/task:0/gpu:0"](Variable_4, save/restore_slice_9/_12)]] 

Answer 1

我认为这个问题是您需要删除这个从W_B然后将其保存，然后恢复它作为你在干什么。

删除此：

'weight_4': tf.Variable(tf.random_normal([num_hidden, num_labels], stddev=0.1)), 

那么它应该工作。主要原因是你正在改变标签的数量，并希望它恢复到同一个变量。作为一个方面说明，最好使用tf.get_variable而不是tf.Variable。

---

更新答案：

创建一个名为

w_b_to_save = { 
'weight_0': tf.Variable(tf.random_normal([patch_size_1, patch_size_1, num_channels, depth],stddev=0.1)), 
'weight_1': tf.Variable(tf.random_normal([patch_size_2, patch_size_2, depth, depth], stddev=0.1)), 
'weight_2': tf.Variable(tf.random_normal([patch_size_3, patch_size_3, depth, depth], stddev=0.1)), 
'weight_3': tf.Variable(tf.random_normal([IMAGE_SIZE_H // 32 * IMAGE_SIZE_W // 32 * depth, num_hidden], stddev=0.1)), 


'bias_0' : tf.Variable(tf.zeros([depth])), 
'bias_1' : tf.Variable(tf.constant(1.0, shape=[depth])), 
'bias_2' : tf.Variable(tf.constant(1.0, shape=[depth])), 
'bias_3' : tf.Variable(tf.constant(1.0, shape=[num_hidden])), 
    } 

... 

w_b_saver = tf.train.Saver(var_list = w_b_to_save) 

新的变量，现在你就可以保存你想要的人。创建一个与上一个基本相同的新变量有点过分，但它显示了不能同时保存最后一个图层的点，并在修改它时进行恢复。